Difference between revisions of "Performance/vConsolidate-SMP"
Vlukovnikov (talk | contribs) (Created page with '=== Benchmark Description === This benchmark is designed to measure aggregated server performance in consolidation scenario: where different apps are running at the same time in…') |
m (Reverted edits by 18.85.56.20 (talk) to last revision by Vlukovnikov) |
||
| (5 intermediate revisions by 3 users not shown) | |||
| Line 1: | Line 1: | ||
=== Benchmark Description === | === Benchmark Description === | ||
| − | + | The goal of Intel vConsolidate benchmark is to measure aggregated server performance in consolidation scenario: when different (and non-related unlike to LAMP benchmark) applications are running on the same box each in its own virtual environment (virtual machine or container). | |
| + | vConsolidate benchmark was developed by Intel in cooperation with other vendors. It runs separate workloads with Java (SPECjbb test), Mail, Web and Database VMs running concurrently. Each set of such VMs is called CSU - Consolidation Stack Unit. Performance metric is a geomean from throughput of each workload type: transactions/sec for Db, requests/sec for Web and java operations/sec for Java. The same type of metric is commonly used in other benchmarks like those from SPEC. | ||
| + | |||
| + | vConsolidate benchmark is very similar to other virtualization specific benchmarks like VMMark from VMWare and SPECvirt, but the latter are more enterprise oriented and generate the load requiring a fast SAN storage and high end hardware. Since we believe containers benefits are even more prominent on commodity hardware we use vConsolidate benchmark to demonstrate that. | ||
=== Implementation === | === Implementation === | ||
| − | + | Intel vConsolidate v1.1. We do not run Mail VM as Intel benchmark has Microsoft Windows version only for mail workload. | |
| − | |||
=== Testbed Configuration === | === Testbed Configuration === | ||
| − | Server: 4xHexCore Intel Xeon (2.66 GHz), 64 GB RAM, HP MSA1500 SAN Storage, 8 SATA (7200 RPM) Disks in RAID0 | + | Hardware: |
| + | * Server: 4xHexCore Intel Xeon (2.66 GHz), 64 GB RAM, HP MSA1500 SAN Storage, 8 SATA (7200 RPM) Disks in RAID0 | ||
| + | * Client: 4xHexCore Intel Xeon (2.136 GHz), 32 GB RAM | ||
| + | * Network: 1Gbit direct server <-> client connection | ||
| − | + | Platform: | |
| − | + | * Virtualization Software: ESXi4.1up1, XenServer5.6fp1, OpenVZ (RH5) 2.6.18-028stab090.4, OpenVZ (RH6) 2.6.32-042test006.1.x86_64 | |
| − | + | * Guest OS: Centos 5.5 x86_64 | |
| − | |||
| − | Virtualization Software: XenServer5.6fp1, OpenVZ (RH5) 2.6.18- | ||
| − | |||
| − | Guest OS: Centos 5.5 x86_64 | ||
Software and Tunings: | Software and Tunings: | ||
| + | * Each VM/CT was configured with 4 vCPUs, 1 GB RAM | ||
| + | * Custom vConsolidate profile was used: 4 load threads for Java workload, 4 load threads for Db workload and 8 threads for Web workload (standard setting) | ||
* Firewall was turned off | * Firewall was turned off | ||
| − | * All other | + | * All other settings were left as defaults |
=== Benchmark Results === | === Benchmark Results === | ||
| Line 30: | Line 33: | ||
=== Summary === | === Summary === | ||
| − | *TODO | + | OpenVZ demonstrates outstanding performance benefits over hypervisors in case of multi-processor environments and multi-threaded workloads: |
| + | * Almost 3x times better overall performance in case of single set of workloads (1 CSU). | ||
| + | * 2.1-2.2x times better overall performance on 5 and 10 CSU. | ||
| + | |||
| + | === TODO === | ||
| + | * add a link to paper where explaining why containers are great for SMP workloads and CPU overcommit. | ||
Latest revision as of 05:23, 28 November 2011
Contents
Benchmark Description[edit]
The goal of Intel vConsolidate benchmark is to measure aggregated server performance in consolidation scenario: when different (and non-related unlike to LAMP benchmark) applications are running on the same box each in its own virtual environment (virtual machine or container).
vConsolidate benchmark was developed by Intel in cooperation with other vendors. It runs separate workloads with Java (SPECjbb test), Mail, Web and Database VMs running concurrently. Each set of such VMs is called CSU - Consolidation Stack Unit. Performance metric is a geomean from throughput of each workload type: transactions/sec for Db, requests/sec for Web and java operations/sec for Java. The same type of metric is commonly used in other benchmarks like those from SPEC.
vConsolidate benchmark is very similar to other virtualization specific benchmarks like VMMark from VMWare and SPECvirt, but the latter are more enterprise oriented and generate the load requiring a fast SAN storage and high end hardware. Since we believe containers benefits are even more prominent on commodity hardware we use vConsolidate benchmark to demonstrate that.
Implementation[edit]
Intel vConsolidate v1.1. We do not run Mail VM as Intel benchmark has Microsoft Windows version only for mail workload.
Testbed Configuration[edit]
Hardware:
- Server: 4xHexCore Intel Xeon (2.66 GHz), 64 GB RAM, HP MSA1500 SAN Storage, 8 SATA (7200 RPM) Disks in RAID0
- Client: 4xHexCore Intel Xeon (2.136 GHz), 32 GB RAM
- Network: 1Gbit direct server <-> client connection
Platform:
- Virtualization Software: ESXi4.1up1, XenServer5.6fp1, OpenVZ (RH5) 2.6.18-028stab090.4, OpenVZ (RH6) 2.6.32-042test006.1.x86_64
- Guest OS: Centos 5.5 x86_64
Software and Tunings:
- Each VM/CT was configured with 4 vCPUs, 1 GB RAM
- Custom vConsolidate profile was used: 4 load threads for Java workload, 4 load threads for Db workload and 8 threads for Web workload (standard setting)
- Firewall was turned off
- All other settings were left as defaults
Benchmark Results[edit]
Summary[edit]
OpenVZ demonstrates outstanding performance benefits over hypervisors in case of multi-processor environments and multi-threaded workloads:
- Almost 3x times better overall performance in case of single set of workloads (1 CSU).
- 2.1-2.2x times better overall performance on 5 and 10 CSU.
TODO[edit]
- add a link to paper where explaining why containers are great for SMP workloads and CPU overcommit.
